Mendeley
TY  _  JOUR
ID  -  2021051316105
TI  -  Optimized kernel Nonparametric Weighted Feature Extraction for Hyperspectral Image Classification
JO  -  Journal of Information Systems and Telecommunication (JIST) 
JA  -  ES
LA  -  en
SN  -  2322-1437
AU  -  Hasheminejad Mohammad
AD  -  University of Jiroft
Y1  -  2022
PY  -  2022
VL  -  38
IS  -  
SP  -  111
EP  -  119
KW  -  Feature Extraction
KW  -   Image Classification
KW  -   Optimized KNWFE
KW  -   Hyperspectral
KW  -   kernel
DO  -  
N2  -  Hyperspectral image (HSI) classification is an essential means of the analysis of remotely sensed images. Remote sensing of natural resources, astronomy, medicine, agriculture, food health, and many other applications are examples of possible applications of this technique. Since hyperspectral images contain redundant measurements, it is crucial to identify a subset of efficient features for modeling the classes. Kernel-based methods are widely used in this field. In this paper, we introduce a new kernel-based method that defines Hyperplane more optimally than previous methods. The presence of noise data in many kernel-based HSI classification methods causes changes in boundary samples and, as a result, incorrect class hyperplane training. We propose the optimized kernel non-parametric weighted feature extraction for hyperspectral image classification. KNWFE is a kernel-based feature extraction method, which has promising results in classifying remotely-sensed image data.  However, it does not take the closeness or distance of the data to the target classes. Solving the problem, we propose optimized KNWFE, which results in better classification performance. Our extensive experiments show that the proposed method improves the accuracy of HSI classification and is superior to the state-of-the-art HIS classifiers.
UR  - rimag.ir/en/Article/16105
L1  - rimag.ir/en/Article/Download/16105
ER  -  
